Semiblind source separation of climate data detects El Nino as the component with the highest interannual variability

Denoising source separation (DSS), a recently developed source separation framework, was applied to extracting components exhibiting slow, interannual temporal behavior from climate data. Three datasets with daily measurements were used: surface temperature, sea level pressure and precipitation around the globe. For all datasets, the first extracted component captured the well-known El Nino-Southern Oscillation phenomenon and the second component was close to the derivative of the first one. Several other components with slow dynamics were extracted and together the components appear to capture essential features of the slow-dynamics state of the climate system. The first two components were identified reliably but the following components may have remained mixed, nonlinear DSS could identify the physically most meaningful rotation among them but only linear DSS was within the scope of this paper. This paper offers a simple demonstration of exploratory data analysis of climate data by DSS and suggests future lines of research.